{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "44b7bc22",
   "metadata": {},
   "source": [
    "# 0. 环境准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "004518a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# 设置pandas可以显示的行数和列数\n",
    "pd.options.display.max_rows = 400\n",
    "pd.options.display.max_columns = None\n",
    "\n",
    "# 忽略warnings\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "#推荐安装插件： nbextensions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b92f26ef",
   "metadata": {},
   "source": [
    "# 1.读入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4a9c551c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>store</th>\n",
       "      <th>dept</th>\n",
       "      <th>week</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-01</td>\n",
       "      <td>24924.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-08</td>\n",
       "      <td>46039.49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   store  dept       week     sales\n",
       "0      1     1 2010-02-01  24924.50\n",
       "1      1     1 2010-02-08  46039.49"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# store:门店编号\n",
    "# dept: 商品部门编号\n",
    "# week: 每周周一的日期 \n",
    "# sales: 销售金额\n",
    "df_sales = pd.read_csv('data/store_sales.csv', parse_dates=['week'])\n",
    "df_sales.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "092fa2c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>store</th>\n",
       "      <th>dept</th>\n",
       "      <th>week</th>\n",
       "      <th>promotion_sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-08</td>\n",
       "      <td>22538.074165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-15</td>\n",
       "      <td>18381.721909</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   store  dept       week  promotion_sales\n",
       "0      1     1 2010-02-08     22538.074165\n",
       "1      1     1 2010-02-15     18381.721909"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# store:门店编号\n",
    "# dept: 商品部门编号\n",
    "# week: 每周周一的日期 \n",
    "# promotion_sales: 促销活动带来的销售金额\n",
    "df_promotion = pd.read_csv('data/promotion_data.csv', parse_dates=['week'])\n",
    "df_promotion.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7134680f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>store</th>\n",
       "      <th>dept</th>\n",
       "      <th>week</th>\n",
       "      <th>sales</th>\n",
       "      <th>promotion_sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-01</td>\n",
       "      <td>24924.50</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-08</td>\n",
       "      <td>46039.49</td>\n",
       "      <td>22538.074165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-15</td>\n",
       "      <td>41595.55</td>\n",
       "      <td>18381.721909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-22</td>\n",
       "      <td>19403.54</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-03-01</td>\n",
       "      <td>21827.90</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   store  dept       week     sales  promotion_sales\n",
       "0      1     1 2010-02-01  24924.50         0.000000\n",
       "1      1     1 2010-02-08  46039.49     22538.074165\n",
       "2      1     1 2010-02-15  41595.55     18381.721909\n",
       "3      1     1 2010-02-22  19403.54         0.000000\n",
       "4      1     1 2010-03-01  21827.90         0.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 合并销售和促销数据\n",
    "df_all = pd.merge( df_sales, df_promotion, how='left' )\n",
    "df_all.fillna(0, inplace=True)\n",
    "df_all.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80809353",
   "metadata": {},
   "source": [
    "# 使用prophet算法做预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2fc19f33",
   "metadata": {},
   "outputs": [],
   "source": [
    "import prophet"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63f8de68",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## 预测1号店的1号部门， 以2012-07-30以前的数据做训练，往后预测一周"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c35e0752",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# 数据准备\n",
    "df_train = df_all[ (df_all['week']<='2012-07-30') & \n",
    "                 (df_all['store']==1) & \n",
    "                 (df_all['dept']==1)]\n",
    "\n",
    "#使用prophet前，要将日期字段重命名为”ds\", 预测对象重命名为\"y\"\n",
    "df_train.rename(columns={'week':'ds','sales':'y'},inplace=True)\n",
    "\n",
    "df_test = df_all[(df_all['week']=='2012-08-06') &\n",
    "                (df_all['store']==1) & \n",
    "                (df_all['dept']==1)]\n",
    "df_test.rename(columns={'week':'ds','sales':'y'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2c1dbec2",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "16:28:30 - cmdstanpy - INFO - Chain [1] start processing\n",
      "16:28:30 - cmdstanpy - INFO - Chain [1] done processing\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<prophet.forecaster.Prophet at 0x1f7301302c8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#训练模型\n",
    "m = prophet.Prophet(yearly_seasonality=True)\n",
    "m.add_regressor( 'promotion_sales' )\n",
    "m.fit( df_train )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "557bb1a5",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x648 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  拟合历史的数据\n",
    "df_fit = m.predict( df_train )\n",
    "\n",
    "# 可视化拟合结果\n",
    "fig1 = m.plot_components(df_fit)\n",
    "fig2 = m.plot( df_fit )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "aef02e8e",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# 预测未来的数据\n",
    "df_predict = m.predict( df_test )\n",
    "df_test['yhat'] = df_predict['yhat'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4948d91",
   "metadata": {},
   "source": [
    "## 预测1号店的所有部门， 以2012-07-30以前的数据做训练，往后预测一周"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c825f372",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "16:33:55 - cmdstanpy - INFO - Chain [1] start processing\n",
      "16:34:35 - cmdstanpy - INFO - Chain [1] start processing\n",
      "16:34:35 - cmdstanpy - INFO - Chain [1] done processing\n"
     ]
    }
   ],
   "source": [
    "dept_list = df_all[ df_all['store']==1 ]['dept'].unique()\n",
    "\n",
    "all_result = []\n",
    "for dept in dept_list:\n",
    "    # 数据准备\n",
    "    df_train = df_all[ (df_all['week']<='2012-07-30') & \n",
    "                      (df_all['store']==1) & \n",
    "                      (df_all['dept']==dept)]\n",
    "    df_train.rename(columns={'week':'ds','sales':'y'},inplace=True)\n",
    "\n",
    "    df_test = df_all[ (df_all['week']>'2012-07-30') & \n",
    "                     (df_all['week']<='2012-08-06') &\n",
    "                     (df_all['store']==1) & \n",
    "                     (df_all['dept']==dept)]\n",
    "    df_test.rename(columns={'week':'ds','sales':'y'},inplace=True)\n",
    "    \n",
    "    ## 只有超过两年的历史数据才能训练模型\n",
    "    if (df_train.shape[0] > 100) & ( df_test.shape[0] > 0 ):\n",
    "        #训练模型\n",
    "        m = prophet.Prophet(yearly_seasonality=True)\n",
    "        m.add_regressor( 'promotion_sales' )\n",
    "        m.fit( df_train )\n",
    "\n",
    "        #预测结果\n",
    "        df_predict = m.predict( df_test )\n",
    "        df_test['yhat'] = df_predict['yhat'].values\n",
    "\n",
    "        all_result.append( df_test )\n",
    "all_result =pd.concat( all_result )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1823ff19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mape 0.056866876161314785\n"
     ]
    }
   ],
   "source": [
    "# 计算mape\n",
    "all_result['percentage_error'] = abs(all_result['yhat']-all_result['y'])/all_result['y']\n",
    "#通常我们用y值作为权重，计算加权平均误差\n",
    "print(\"mape\", np.sum( all_result['percentage_error']*all_result['y'] )/np.sum( all_result['y'] ) ) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f894266b",
   "metadata": {},
   "source": [
    "# 使用lightGBM算法做预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e66e949a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import lightgbm as lgb"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55321b0a",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## 预测1号店的1号部门， 以2012-07-30以前的数据做训练，往后预测一周"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ace128d2",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "df_sample = df_all[ (df_all['store']==1) & \n",
    "                  (df_all['dept']==1)].sort_values('week')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9bf7cdf4",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>store</th>\n",
       "      <th>dept</th>\n",
       "      <th>week</th>\n",
       "      <th>sales</th>\n",
       "      <th>promotion_sales</th>\n",
       "      <th>sales_lw</th>\n",
       "      <th>promotion_lw</th>\n",
       "      <th>sales_ly</th>\n",
       "      <th>promotion_ly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-31</td>\n",
       "      <td>21665.76</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18461.18</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24924.50</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-02-07</td>\n",
       "      <td>37887.17</td>\n",
       "      <td>14215.147762</td>\n",
       "      <td>21665.76</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>46039.49</td>\n",
       "      <td>22538.074165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-02-14</td>\n",
       "      <td>46845.87</td>\n",
       "      <td>23510.085530</td>\n",
       "      <td>37887.17</td>\n",
       "      <td>14215.147762</td>\n",
       "      <td>41595.55</td>\n",
       "      <td>18381.721909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-02-21</td>\n",
       "      <td>19363.83</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>46845.87</td>\n",
       "      <td>23510.085530</td>\n",
       "      <td>19403.54</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-02-28</td>\n",
       "      <td>20327.61</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19363.83</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21827.90</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    store  dept       week     sales  promotion_sales  sales_lw  promotion_lw  \\\n",
       "52      1     1 2011-01-31  21665.76         0.000000  18461.18      0.000000   \n",
       "53      1     1 2011-02-07  37887.17     14215.147762  21665.76      0.000000   \n",
       "54      1     1 2011-02-14  46845.87     23510.085530  37887.17  14215.147762   \n",
       "55      1     1 2011-02-21  19363.83         0.000000  46845.87  23510.085530   \n",
       "56      1     1 2011-02-28  20327.61         0.000000  19363.83      0.000000   \n",
       "\n",
       "    sales_ly  promotion_ly  \n",
       "52  24924.50      0.000000  \n",
       "53  46039.49  22538.074165  \n",
       "54  41595.55  18381.721909  \n",
       "55  19403.54      0.000000  \n",
       "56  21827.90      0.000000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 特征构建\n",
    "feature_cols = []\n",
    "\n",
    "## 第一组特征： 历史数据最后一周的销量和促销活动的金额\n",
    "df_sample['sales_lw'] = df_sample['sales'].shift(1)\n",
    "df_sample['promotion_lw'] = df_sample['promotion_sales'].shift(1)\n",
    "feature_cols = feature_cols + ['sales_lw', 'promotion_lw']\n",
    "\n",
    "## 第二组特征： 上一个周期（即去年同一周）的销量和促销活动金额\n",
    "df_sample['sales_ly'] = df_sample['sales'].shift(52)\n",
    "df_sample['promotion_ly'] = df_sample['promotion_sales'].shift(52)\n",
    "feature_cols = feature_cols + ['sales_ly', 'promotion_ly']\n",
    "\n",
    "## 第三组特征：待预测周的促销活动金额\n",
    "feature_cols = feature_cols + ['promotion_sales']\n",
    "\n",
    "## 只保留所有特征都不为空的数据\n",
    "for col in feature_cols:\n",
    "    df_sample = df_sample[ ~df_sample[col].isna() ]\n",
    "df_sample.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9038e811",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# 构建训练集和验证集\n",
    "x_train = df_sample[ df_sample['week']<='2012-07-30' ][ feature_cols ].values\n",
    "y_train = df_sample[ df_sample['week']<='2012-07-30' ][ 'sales' ].values\n",
    "\n",
    "x_test = df_sample[ df_sample['week']=='2012-08-06' ][ feature_cols ].values\n",
    "y_test = df_sample[ df_sample['week']=='2012-08-06'  ][ 'sales' ].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "631fe2e5",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LGBMRegressor()"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用lightGBM建模\n",
    "from lightgbm.sklearn import LGBMRegressor\n",
    "\n",
    "model = LGBMRegressor()\n",
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "30d61aa4",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# 预测\n",
    "y_pred = model.predict( x_test )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f5be6400",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([16165.25781465]), array([16119.92]))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred, y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21f8be76",
   "metadata": {},
   "source": [
    "## 预测1号店的所有部门， 以2012-07-30以前的数据做训练，往后预测一周"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3ee60685",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sample = df_all[ (df_all['store']==1)].sort_values(['dept','week'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "82473c40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>store</th>\n",
       "      <th>dept</th>\n",
       "      <th>week</th>\n",
       "      <th>sales</th>\n",
       "      <th>promotion_sales</th>\n",
       "      <th>sales_lw</th>\n",
       "      <th>promotion_lw</th>\n",
       "      <th>sales_ly</th>\n",
       "      <th>promotion_ly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-31</td>\n",
       "      <td>21665.76</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18461.18</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24924.50</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-02-07</td>\n",
       "      <td>37887.17</td>\n",
       "      <td>14215.147762</td>\n",
       "      <td>21665.76</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>46039.49</td>\n",
       "      <td>22538.074165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-02-14</td>\n",
       "      <td>46845.87</td>\n",
       "      <td>23510.085530</td>\n",
       "      <td>37887.17</td>\n",
       "      <td>14215.147762</td>\n",
       "      <td>41595.55</td>\n",
       "      <td>18381.721909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-02-21</td>\n",
       "      <td>19363.83</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>46845.87</td>\n",
       "      <td>23510.085530</td>\n",
       "      <td>19403.54</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-02-28</td>\n",
       "      <td>20327.61</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19363.83</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21827.90</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    store  dept       week     sales  promotion_sales  sales_lw  promotion_lw  \\\n",
       "52      1     1 2011-01-31  21665.76         0.000000  18461.18      0.000000   \n",
       "53      1     1 2011-02-07  37887.17     14215.147762  21665.76      0.000000   \n",
       "54      1     1 2011-02-14  46845.87     23510.085530  37887.17  14215.147762   \n",
       "55      1     1 2011-02-21  19363.83         0.000000  46845.87  23510.085530   \n",
       "56      1     1 2011-02-28  20327.61         0.000000  19363.83      0.000000   \n",
       "\n",
       "    sales_ly  promotion_ly  \n",
       "52  24924.50      0.000000  \n",
       "53  46039.49  22538.074165  \n",
       "54  41595.55  18381.721909  \n",
       "55  19403.54      0.000000  \n",
       "56  21827.90      0.000000  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 特征构建\n",
    "feature_cols = []\n",
    "\n",
    "## 第一组特征： 历史数据最后一周的销量和促销活动的金额\n",
    "df_sample['sales_lw'] = df_sample.groupby(['dept'])['sales'].shift(1)\n",
    "df_sample['promotion_lw'] = df_sample.groupby(['dept'])['promotion_sales'].shift(1)\n",
    "feature_cols = feature_cols + ['sales_lw', 'promotion_lw']\n",
    "\n",
    "## 第二组特征： 上一个周期（即去年同一周）的销量和促销活动金额\n",
    "df_sample['sales_ly'] = df_sample.groupby(['dept'])['sales'].shift(52)\n",
    "df_sample['promotion_ly'] = df_sample.groupby(['dept'])['promotion_sales'].shift(52)\n",
    "feature_cols = feature_cols + ['sales_ly', 'promotion_ly']\n",
    "\n",
    "## 第三组特征：待预测周的促销活动金额\n",
    "feature_cols = feature_cols + ['promotion_sales']\n",
    "\n",
    "## 只保留所有特征都不为空的数据\n",
    "for col in feature_cols:\n",
    "    df_sample = df_sample[ ~df_sample[col].isna() ]\n",
    "df_sample.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0b368517",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 构建训练集和验证集\n",
    "x_train = df_sample[ df_sample['week']<='2012-07-30' ][ feature_cols ].values\n",
    "y_train = df_sample[ df_sample['week']<='2012-07-30' ][ 'sales' ].values\n",
    "\n",
    "x_test = df_sample[ df_sample['week']=='2012-08-06' ][ feature_cols ].values\n",
    "y_test = df_sample[ df_sample['week']=='2012-08-06'  ][ 'sales' ].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7ca1f1ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LGBMRegressor()"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用lightGBM建模\n",
    "model = LGBMRegressor()\n",
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "42d31122",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 预测\n",
    "y_pred = model.predict( x_test )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8dc5add4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 15827.41919404,  46492.69038445,  33816.23715648,  38767.89005671,\n",
       "         18651.50044702,   3007.55989147,  15723.48993843,  36569.28449712,\n",
       "         22201.31808872,  32237.02102998,  25665.66996911,  11427.83049287,\n",
       "         39267.89433303,  12809.91590551,  23056.1242317 ,   9415.96940694,\n",
       "           893.43898638,   4313.50313049,   6611.0256935 ,   7329.36145893,\n",
       "         20837.83094154,   7810.59937471,   8883.57350279,   4828.58999793,\n",
       "           956.34503643,    362.19617876,   4384.56222533,   3335.2270923 ,\n",
       "          4473.93186268,   5600.11983861,   8499.64189403,  11254.18765046,\n",
       "          1941.91176181,   1327.42045054,   2698.46876661,  71375.85983846,\n",
       "         53906.44281667,    420.61066405,   8576.46688884,   4353.24002767,\n",
       "         19717.37652578,    526.81136791,  13935.96711862,   1600.15559869,\n",
       "           204.92894605,   8153.33735333,   1222.09211416,   2779.01344178,\n",
       "           544.60686348,    860.24273607,   5682.3938113 ,   3256.49984248,\n",
       "         39797.13819677,  12012.64557606,  31244.90039141,  19154.65006228,\n",
       "         31333.31121961,  19744.77940377,   7672.37816842,   3145.46527063,\n",
       "         47094.2301211 ,  81128.12550633,  73347.21315738, 154961.32402886,\n",
       "         76909.66897362,  69299.39472149, 151989.87363041,  36547.82478782,\n",
       "         36770.50833219,  10954.1012952 ]),\n",
       " array([ 1.6119920e+04,  4.6729910e+04,  2.8257300e+04,  4.0343830e+04,\n",
       "         1.8232460e+04, -1.3965000e+02,  1.6552490e+04,  3.7269920e+04,\n",
       "         2.0400880e+04,  3.3834220e+04,  2.4145930e+04,  1.0605660e+04,\n",
       "         4.2241230e+04,  1.2434690e+04,  2.1677850e+04,  8.4413400e+03,\n",
       "         8.7510000e+02,  4.5343700e+03,  6.7528500e+03,  7.2299400e+03,\n",
       "         2.1599340e+04,  8.8702300e+03,  8.6146700e+03,  5.4989000e+03,\n",
       "         1.1221400e+03,  3.0787000e+02,  4.1624600e+03,  3.3553400e+03,\n",
       "         4.4642500e+03,  5.7969500e+03,  9.0302500e+03,  1.0609610e+04,\n",
       "         2.0185100e+03,  8.8100000e+02,  2.8212000e+03,  7.4483320e+04,\n",
       "         5.7803560e+04,  5.6530000e+02,  8.7444900e+03,  4.7054500e+03,\n",
       "         1.7599960e+04,  7.9600000e+02,  1.3369910e+04,  1.3423500e+03,\n",
       "         4.9800000e+01,  7.8610100e+03,  9.2583000e+02,  1.4200000e+03,\n",
       "         1.5207000e+02,  1.0080000e+03,  5.4419800e+03,  3.2407200e+03,\n",
       "         4.0663950e+04,  1.1995610e+04,  3.5436110e+04,  1.9874450e+04,\n",
       "         3.1933130e+04,  1.8532350e+04,  7.5475100e+03,  2.8689800e+03,\n",
       "         5.1962160e+04,  8.9889860e+04,  7.3659910e+04,  1.5001251e+05,\n",
       "         8.4119120e+04,  6.5343750e+04,  1.3740820e+05,  3.4261420e+04,\n",
       "         4.0136610e+04,  1.1493610e+04]))"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred, y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b73fbee",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
